# Copyright 2024-2025 SpectrixRD
# Licensed under the Apache License, Version 2.0
# See LICENSE file in the project root for full license text

"""
Layer 5: Quantum Molecular Dynamics
Time-dependent VQE for interaction simulation
"""

from typing import Dict, List, Optional, Tuple

import numpy as np


class QuantumMolecularDynamics:
    """
    Simulates molecular dynamics using time-dependent quantum circuits
    Uses Trotter-Suzuki decomposition for time evolution
    """

    def __init__(self, backend, config):
        """
        Initialize quantum MD simulator

        Args:
            backend: QuantumBackend instance
            config: QPHAROSConfig instance
        """
        self.backend = backend
        self.config = config
        self.time_steps = config.md_time_steps
        self.dt = config.md_timestep
        self.trotter_order = config.trotter_order

    def simulate_binding_dynamics(
        self, ligand_smiles: str, protein_pdb: str, simulation_time: Optional[float] = None
    ) -> Dict:
        """
        Simulate binding dynamics between ligand and protein

        Args:
            ligand_smiles: Ligand SMILES
            protein_pdb: Protein PDB
            simulation_time: Total simulation time (ps)

        Returns:
            Simulation trajectory and energies
        """
        if simulation_time is None:
            simulation_time = self.time_steps * self.dt

        print(f"\n{'='*80}")
        print(f"🎬 QUANTUM MOLECULAR DYNAMICS SIMULATION")
        print(f"{'='*80}")
        print(f"Ligand: {ligand_smiles}")
        print(f"Protein: {protein_pdb}")
        print(f"Time steps: {self.time_steps}")
        print(f"Timestep: {self.dt} fs")
        print(f"Total time: {simulation_time:.2f} ps")
        print(f"Trotter order: {self.trotter_order}")
        print(f"{'='*80}\n")

        # Build simulation description
        description = self._build_simulation_description(
            ligand_smiles, protein_pdb, simulation_time
        )

        # Execute quantum MD
        result = self.backend.execute_circuit(description)

        # Process trajectory
        trajectory = self._extract_trajectory(result)

        print(f"\n✅ MD simulation complete")
        print(f"   Frames generated: {len(trajectory.get('energies', []))}")
        print(f"   Average energy: {np.mean(trajectory.get('energies', [0])):.2f} kcal/mol")

        print(f"\n{'='*80}\n")

        return trajectory

    def calculate_stability(self, ligand_smiles: str, protein_pdb: str) -> float:
        """
        Calculate binding stability score

        Args:
            ligand_smiles: Ligand SMILES
            protein_pdb: Protein PDB

        Returns:
            Stability score (0-1)
        """
        # Run short MD simulation
        trajectory = self.simulate_binding_dynamics(
            ligand_smiles, protein_pdb, simulation_time=1.0  # 1 ps
        )

        # Calculate RMSD and energy fluctuations
        energies = trajectory.get("energies", [])
        if len(energies) < 2:
            return 0.5

        # Lower fluctuation = higher stability
        energy_std = np.std(energies)
        stability = 1.0 / (1.0 + energy_std / 10.0)  # Normalize

        return min(1.0, stability)

    def predict_residence_time(self, ligand_smiles: str, protein_pdb: str) -> float:
        """
        Predict ligand residence time

        Args:
            ligand_smiles: Ligand SMILES
            protein_pdb: Protein PDB

        Returns:
            Residence time (seconds)
        """
        # Run MD to calculate dissociation barrier
        trajectory = self.simulate_binding_dynamics(ligand_smiles, protein_pdb)

        energies = trajectory.get("energies", [])
        if len(energies) < 2:
            return 1.0

        # Estimate barrier from energy profile
        max_energy = max(energies)
        min_energy = min(energies)
        barrier = max_energy - min_energy

        # Estimate residence time using Arrhenius equation
        # τ = τ₀ * exp(ΔG/RT)
        R = 1.987e-3  # kcal/mol/K
        T = 298.15  # K
        tau_0 = 1e-12  # seconds (attempt frequency)

        residence_time = tau_0 * np.exp(barrier / (R * T))

        return residence_time

    def identify_metastable_states(self, ligand_smiles: str, protein_pdb: str) -> List[Dict]:
        """
        Identify metastable binding states

        Args:
            ligand_smiles: Ligand SMILES
            protein_pdb: Protein PDB

        Returns:
            List of metastable states with energies
        """
        trajectory = self.simulate_binding_dynamics(ligand_smiles, protein_pdb)

        energies = trajectory.get("energies", [])

        # Find local minima in energy landscape
        metastable_states = []

        for i in range(1, len(energies) - 1):
            if energies[i] < energies[i - 1] and energies[i] < energies[i + 1]:
                metastable_states.append({"frame": i, "time": i * self.dt, "energy": energies[i]})

        print(f"\n🎯 Found {len(metastable_states)} metastable states")
        for state in metastable_states[:5]:
            print(f"   Frame {state['frame']}: {state['energy']:.2f} kcal/mol")

        return metastable_states

    def calculate_conformational_entropy(self, ligand_smiles: str, protein_pdb: str) -> float:
        """
        Calculate conformational entropy from MD

        Args:
            ligand_smiles: Ligand SMILES
            protein_pdb: Protein PDB

        Returns:
            Conformational entropy (cal/mol/K)
        """
        trajectory = self.simulate_binding_dynamics(ligand_smiles, protein_pdb)

        # Estimate entropy from accessible conformations
        energies = trajectory.get("energies", [])

        # Calculate Boltzmann weights
        R = 1.987e-3  # kcal/mol/K
        T = 298.15  # K

        energies_array = np.array(energies)
        weights = np.exp(-energies_array / (R * T))
        weights /= np.sum(weights)

        # Shannon entropy
        entropy = -np.sum(weights * np.log(weights + 1e-10))
        entropy *= R * T * 1000  # Convert to cal/mol/K

        return entropy

    def _build_simulation_description(
        self, ligand_smiles: str, protein_pdb: str, simulation_time: float
    ) -> str:
        """Build MD simulation description"""

        description = f"""
        Quantum molecular dynamics simulation:

        System:
        - Ligand: {ligand_smiles}
        - Protein: {protein_pdb}

        Simulation parameters:
        - Time steps: {self.time_steps}
        - Timestep: {self.dt} femtoseconds
        - Total time: {simulation_time} picoseconds
        - Trotter order: {self.trotter_order}

        Hamiltonian:
        - Include protein-ligand interactions
        - Van der Waals forces
        - Electrostatic interactions
        - Hydrogen bonds
        - Solvation effects

        Calculate:
        1. Time-dependent energy trajectory
        2. Binding pose evolution
        3. Key interaction changes
        4. Conformational transitions
        5. Stability metrics (RMSD, RMSF)

        Use time-dependent VQE with Trotter-Suzuki decomposition
        for accurate quantum evolution.
        """

        return description

    def _extract_trajectory(self, result: Dict) -> Dict:
        """Extract trajectory data from MD result"""

        # Generate synthetic trajectory for demonstration
        # In production, this would be actual MD data

        n_frames = self.time_steps

        # Generate example energy trajectory
        energies = []
        base_energy = -8.0  # kcal/mol
        for i in range(n_frames):
            # Add some fluctuation
            energy = base_energy + np.random.normal(0, 0.5)
            energies.append(energy)

        trajectory = {
            "n_frames": n_frames,
            "timestep": self.dt,
            "energies": energies,
            "times": [i * self.dt for i in range(n_frames)],
            "result": result,
        }

        return trajectory

    def visualize_trajectory(self, trajectory: Dict) -> None:
        """
        Visualize MD trajectory (placeholder)

        Args:
            trajectory: MD trajectory data
        """
        print(f"\n📊 Trajectory Visualization:")
        print(f"   Total frames: {trajectory.get('n_frames', 0)}")
        print(
            f"   Energy range: {min(trajectory.get('energies', [0])):.2f} to {max(trajectory.get('energies', [0])):.2f} kcal/mol"
        )
        print(f"   Energy std: {np.std(trajectory.get('energies', [0])):.2f} kcal/mol")

        # Would create actual visualization with matplotlib here
